AI Readiness Score
Solid data foundation
Moderate
Good budget fit
Strong automation potential
Realistic
MCP-ready tools
How This System Works
Architecture
This ecommerce automation system implements a two-agent architecture designed to maintain data consistency between Shopify and NetSuite. The Order Reconciler serves as the primary data synchronization agent, running nightly to ensure order information remains consistent across platforms. The Inventory Watchdog provides continuous monitoring of stock levels, alerting stakeholders to potential inventory issues before they impact sales. Both agents operate on scheduled intervals using cron expressions, with the Order Reconciler handling the heavy lifting of data reconciliation during low-traffic hours, while the Inventory Watchdog maintains vigilant oversight throughout business hours. The system utilizes MCP (Model Context Protocol) for secure, reliable data transfer between Shopify and NetSuite, ensuring proper authentication and error handling.
Data Flow
Data flows begin with the Order Reconciler extracting order information from Shopify's REST API, including order details, customer information, and payment status. This data is then transformed into NetSuite's expected format and synchronized using NetSuite's SuiteTalk API. The reconciliation process identifies discrepancies between systems and generates reports for manual review when automated resolution isn't possible. The Inventory Watchdog operates on a separate data flow, querying NetSuite for current stock levels across all SKUs every four hours. When inventory falls below predefined thresholds, the system generates structured alerts sent to designated Slack channels, enabling rapid response from inventory management teams. Both agents maintain detailed logs of their operations, creating an audit trail for compliance and troubleshooting purposes.
Implementation Phases
Establish authentication, logging, and basic connectivity to Shopify and NetSuite APIs
Build and test the complete order synchronization workflow with error handling
Implement stock level monitoring and Slack alerting system
Performance tuning, comprehensive testing, and monitoring setup
Prerequisites
- -Shopify API access with Orders read permissions
- -NetSuite SuiteTalk API access with integration role
- -Slack workspace with bot token and channel access
- -Server environment with Python 3.9+ and cron capability
- -Database for storing reconciliation logs and inventory snapshots
Assumptions
- -Shopify and NetSuite APIs remain stable and accessible
- -Order volume doesn't exceed API rate limits during sync windows
- -Network connectivity is reliable during scheduled operations
- -Inventory thresholds are pre-configured in system settings
- -Manual intervention processes exist for critical reconciliation failures
Recommended Agents (2)
How It Works
- 1Fetch Recent Orders
Connect to Shopify REST API and retrieve all orders created or modified since last sync timestamp, including order status, line items, customer details, and payment information
Shopify REST Admin API - 2Transform Data Format
Convert Shopify order JSON structure to NetSuite SuiteTalk format, mapping customer fields, product SKUs, tax calculations, and shipping information according to predefined business rules
Custom transformation engine - 3Query NetSuite Records
Search NetSuite for existing sales orders using Shopify order numbers as external IDs to identify records requiring updates versus new insertions
NetSuite SuiteTalk API - 4Sync Order Data
Create new sales orders in NetSuite or update existing records, handling customer creation if necessary and maintaining referential integrity across all related records
NetSuite SuiteTalk API - 5Generate Reconciliation Report
Compare synchronized records against source data, identify discrepancies, log all operations, and create summary reports for stakeholder review
Internal logging system
Implementation
# Order Reconciler Implementation
## Directory Structure
```
order_reconciler/
├── src/
│ ├── reconciler.py
│ ├── shopify_client.py
│ ├── netsuite_client.py
│ └── data_transformer.py
├── config/
│ └── settings.json
├── logs/
└── requirements.txt
```
## Key Files
### src/reconciler.py
```python
class OrderReconciler:
def __init__(self):
self.shopify = ShopifyClient()
self.netsuite = NetSuiteClient()
self.transformer = DataTransformer()
def run_sync(self):
last_sync = self.get_last_sync_timestamp()
orders = self.shopify.get_orders_since(last_sync)
for order in orders:
try:
ns_format = self.transformer.shopify_to_netsuite(order)
existing = self.netsuite.find_order(order['name'])
if existing:
self.netsuite.update_order(existing['id'], ns_format)
else:
self.netsuite.create_order(ns_format)
self.log_success(order['name'])
except Exception as e:
self.log_error(order['name'], str(e))
```
### Environment Variables Needed
- SHOPIFY_API_KEY
- SHOPIFY_API_SECRET
- SHOPIFY_ACCESS_TOKEN
- NETSUITE_ACCOUNT_ID
- NETSUITE_CONSUMER_KEY
- NETSUITE_CONSUMER_SECRET
- NETSUITE_TOKEN_ID
- NETSUITE_TOKEN_SECRET
- DATABASE_URL
### Cron Setup
```bash
# Add to crontab for daily 2am execution
0 2 * * * /usr/bin/python3 /path/to/order_reconciler/src/reconciler.py
```Data Flow
Inputs
- Shopify REST API — Order data including customer info, line items, payments, shipping details(JSON)
- NetSuite SuiteTalk API — Existing sales order records for comparison and updates(XML/SOAP)
Outputs
- NetSuite SuiteTalk API — Created or updated sales order records with synchronized data(XML/SOAP)
- Local database — Reconciliation logs, sync timestamps, error reports(SQL records)
Prerequisites
- -Shopify store with API access enabled
- -NetSuite integration role with sales order permissions
- -Database for tracking sync state and logging
- -Python environment with required API libraries
Error Handling
Implement exponential backoff and resume from last successful sync point
Alert administrators immediately and skip current sync cycle
Log problematic order details and continue processing remaining orders
Retry with exponential backoff up to 3 attempts, then abort
Integrations
| Source | Target | Data Flow | Method | Complexity |
|---|---|---|---|---|
| Shopify | NetSuite | Order data sync | mcp | moderate |
Schedule
0 2 * * *Recommended Models
| Task | Recommended | Alternatives | Est. Cost | Why |
|---|---|---|---|---|
| Agent logic / orchestration | Claude Sonnet 4 | GPT-4oGemini 2.5 Pro | $0.003-0.015/call | Complex order reconciliation logic requires structured reasoning to match data across Shopify and NetSuite systems accurately. |
| Data extraction / parsing | Claude Haiku | GPT-4o-miniGemini 2.0 Flash | $0.0002-0.001/call | Fast extraction of order data from Shopify and inventory data from NetSuite APIs with consistent structured output. |
| Content generation | Claude Haiku | GPT-4o-miniGemini 2.0 Flash | $0.0002-0.001/call | Simple alert messages and reconciliation reports for Slack notifications require minimal complexity. |
| Classification / routing | Gemini 2.0 Flash | Claude HaikuGPT-4o-mini | $0.0001-0.001/call | High-volume inventory monitoring needs fast, cheap classification of stock level alerts and discrepancy types. |
ROI Projection
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What's next?
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